R Notebook
Aquarium Temperature Investigation
R script below will subselect and plot temperature data for MOATs Overall goal is to determine if MOATs (per treatment) are true replicates
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## expand, pack, unpack## ************
## Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
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## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
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## combineOutline
(Current as of 2020.06.25)
- 1.) Working Directory
- 2.) Spolling Data into one CSV
- 3.) Creating the Dataframe “dml”
- 4.) Setting a new working directory
- 4.) Creating dateTime objects
- 5.) Creating Treatment Variables
- 6.) Creating Night and Day Periods
- 7.) Insitu data
- 8.) Cleaning up dml “Cdml”
- 9.) Framing filters for Cdml
- 10.) Temperature “Jumps”
- 11.) Calculating averages by treatment & day/night
- 12.) Summary & Group by Cdml
- 13.) Plots Boxplots & timeseries
- 14.) Duration between conditions- Histogram & Cummulative Distribtuion Plot section
- 15.) aTemp bands investig.
#1.) Working Directory
Inside the working directory (folder) are CSV files generated after using the moats graph app Link to moats.Graph.app Moving average window (nObs) set to 4. This results in the observations being averaged into a 24second timeframe. CSVs were created for each moats: M01, M02, M03, M04, M05, M06, M07, M08, M09, M10, M11, M12, M13. Files are also available on the OA Google drive. OA Drive for MOATS data
2.) Spooling Data into one CSV
Combining multiple CSV files into 1 document. Original input files from individual LVM (logical volumne management) files off each MOATs. 2.1 Create a list of files
## 2.1 Create a list of files
# All files to be joined have ext. "csv" can use that pattern to join
files <- list.files(path= "/Users/katherinerovinski/GIT/NWFSC.MUK_MOATs_SMR2019/LabViewLogs(CSV)Rbind", pattern = ".*\\.csv")
print(files)## [1] "M1_df_MoatsData.csv" "M10_df_MoatsData.csv" "M11_df_MoatsData.csv"
## [4] "M12_df_MoatsData.csv" "M13_df_MoatsData.csv" "M2_df_MoatsData.csv"
## [7] "M3_df_MoatsData.csv" "M4_df_MoatsData.csv" "M5_df_MoatsData.csv"
## [10] "M6_df_MoatsData.csv" "M7_df_MoatsData.csv" "M8_df_MoatsData.csv"
## [13] "M9_df_MoatsData.csv"2.2 Create a temporary place for files
## 2.2 Create a temporary place for files
setwd("/Users/katherinerovinski/GIT/NWFSC.MUK_MOATs_SMR2019/LabViewLogs(CSV)Rbind")
temp <- lapply(files, fread, sep= ",")
print(temp)## [[1]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M01 2019-09-05 17:04:00 12.16840 11.324116 0.02244800 9.269025
## 2: M01 2019-09-05 17:04:22 12.17155 11.346000 0.03559150 9.268738
## 3: M01 2019-09-05 17:04:46 12.17656 11.367861 0.02263175 9.268492
## 4: M01 2019-09-05 17:05:10 12.17969 11.396164 0.04348125 9.267310
## 5: M01 2019-09-05 17:05:34 12.18132 11.439413 0.02432750 9.266493
## ---
## 96693: M01 2019-09-17 16:50:23 11.19017 9.521056 0.03718850 9.259634
## 96694: M01 2019-09-17 16:50:48 11.18994 9.509823 0.05128550 9.261294
## 96695: M01 2019-09-19 16:52:00 11.91160 13.162302 7.66178975 8.847867
## 96696: M01 2019-09-19 16:52:21 11.91988 13.164492 7.66264175 8.847612
## 96697: M01 2019-09-19 16:52:45 11.92752 13.168515 7.66253800 8.846189
## salinity
## 1: 29.5
## 2: 29.5
## 3: 29.5
## 4: 29.5
## 5: 29.5
## ---
## 96693: 29.5
## 96694: 29.5
## 96695: 28.8
## 96696: 28.8
## 96697: 28.8
##
## [[2]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M10 2019-09-24 16:05:00 12.08942 10.90406 7.727869 8.892254
## 2: M10 2019-09-24 16:05:23 12.09632 10.88388 7.728531 8.903144
## 3: M10 2019-09-24 16:05:47 12.10228 10.86738 7.729645 8.913513
## 4: M10 2019-09-24 16:06:12 12.10285 10.85012 7.730739 8.923338
## 5: M10 2019-09-24 16:06:35 12.10142 10.84065 7.731811 8.933912
## ---
## 80639: M10 2019-10-30 10:50:59 11.44499 11.70883 7.681737 6.159690
## 80640: M10 2019-10-30 10:53:00 11.50010 11.69634 7.674728 6.176614
## 80641: M10 2019-10-30 10:55:00 11.52432 11.73733 7.673905 6.208007
## 80642: M10 2019-10-30 10:57:00 11.54326 11.78689 7.679314 6.267933
## 80643: M10 2019-10-30 10:58:59 11.52632 11.78225 7.683825 6.351308
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 80639: 28.8
## 80640: 28.8
## 80641: 28.8
## 80642: 28.8
## 80643: 28.8
##
## [[3]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M11 2019-10-28 11:27:00 13.24384 13.27378 7.655841 2.3327030
## 2: M11 2019-10-28 11:27:24 13.24669 13.28229 7.652207 2.4434278
## 3: M11 2019-10-28 11:27:47 13.23905 13.27764 7.654789 2.4257235
## 4: M11 2019-10-28 11:28:12 13.23424 13.26904 7.654091 2.5788760
## 5: M11 2019-10-28 11:28:36 13.22975 13.26124 7.651458 2.6075580
## ---
## 110339: M11 2019-10-30 11:24:59 13.10857 13.14075 7.666257 -0.8970227
## 110340: M11 2019-10-30 11:25:24 13.10800 13.12946 7.669591 -0.8970350
## 110341: M11 2019-10-30 11:25:48 13.10633 13.13023 7.667792 -0.8970733
## 110342: M11 2019-10-30 11:26:12 13.11069 13.13927 7.662918 -0.8969603
## 110343: M11 2019-10-30 11:26:36 13.11461 13.15588 7.659757 -0.8968665
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 110339: 28.8
## 110340: 28.8
## 110341: 28.8
## 110342: 28.8
## 110343: 28.8
##
## [[4]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M12 2019-09-24 10:48:00 11.54457 12.05596 7.635548 6.666244
## 2: M12 2019-09-24 10:48:23 11.55216 12.04850 7.636722 6.681026
## 3: M12 2019-09-24 10:48:47 11.56142 12.05127 7.638049 6.697410
## 4: M12 2019-09-24 10:49:11 11.56440 12.06437 7.639172 6.715560
## 5: M12 2019-09-24 10:49:35 11.57110 12.08176 7.640389 6.734496
## ---
## 54784: M12 2019-11-01 12:43:59 11.16364 11.52935 7.750611 9.125633
## 54785: M12 2019-11-01 12:45:59 11.16199 11.54860 7.751082 9.121086
## 54786: M12 2019-11-01 12:47:59 11.16497 11.57361 7.751337 9.126159
## 54787: M12 2019-11-01 12:49:59 11.17101 11.58790 7.751266 9.143706
## 54788: M12 2019-11-01 12:51:59 11.17299 11.59811 7.750636 9.164638
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 54784: 28.8
## 54785: 28.8
## 54786: 28.8
## 54787: 28.8
## 54788: 28.8
##
## [[5]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M13 2019-10-05 09:42:00 12.88292 12.90622 7.525024 4.209248
## 2: M13 2019-10-05 09:42:24 12.87668 12.91175 7.526525 4.201476
## 3: M13 2019-10-05 09:42:47 12.87611 12.91363 7.528626 4.196523
## 4: M13 2019-10-05 09:43:12 12.88322 12.91140 7.529908 4.195774
## 5: M13 2019-10-05 09:43:36 12.88943 12.90763 7.530890 4.198372
## ---
## 107420: M13 2019-10-31 08:38:00 13.86670 12.98007 7.719215 8.378414
## 107421: M13 2019-10-31 08:39:59 13.85943 12.97847 7.718890 8.376869
## 107422: M13 2019-10-31 08:42:00 13.85518 12.98079 7.719110 8.380475
## 107423: M13 2019-10-31 08:43:59 13.85470 12.97842 7.720154 8.386980
## 107424: M13 2019-10-31 08:46:00 13.85757 12.97633 7.721720 8.395183
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 107420: 28.8
## 107421: 28.8
## 107422: 28.8
## 107423: 28.8
## 107424: 28.8
##
## [[6]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M02 2019-09-24 16:56:00 14.00188 11.88287 7.544728 8.688206
## 2: M02 2019-09-24 16:56:24 13.99150 11.91185 7.544594 8.700327
## 3: M02 2019-09-24 16:56:48 14.00338 11.93261 7.544907 8.711961
## 4: M02 2019-09-24 16:57:12 13.99937 11.95824 7.545630 8.722980
## 5: M02 2019-09-24 16:57:36 13.99001 11.98529 7.546176 8.733106
## ---
## 96770: M02 2019-10-25 13:23:00 13.67577 14.50265 7.553553 7.499465
## 96771: M02 2019-10-25 13:25:00 13.67210 14.50724 7.557383 7.427122
## 96772: M02 2019-10-25 13:27:00 13.67808 14.51468 7.561196 7.381927
## 96773: M02 2019-10-25 13:29:00 13.68171 14.53864 7.565191 7.383728
## 96774: M02 2019-10-25 13:30:59 13.69302 14.58706 7.569311 7.437285
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 96770: 28.8
## 96771: 28.8
## 96772: 28.8
## 96773: 28.8
## 96774: 28.8
##
## [[7]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M03 2019-09-24 16:23:00 12.20874 11.12296 7.716566 8.857545
## 2: M03 2019-09-24 16:23:23 12.21485 11.09998 7.717669 8.858215
## 3: M03 2019-09-24 16:23:47 12.21552 11.07615 7.718555 8.859583
## 4: M03 2019-09-24 16:24:11 12.21425 11.05154 7.719685 8.861280
## 5: M03 2019-09-24 16:24:35 12.21551 11.02739 7.720459 8.862019
## ---
## 103962: M03 2019-10-27 12:35:59 11.18896 11.20713 7.723452 9.457098
## 103963: M03 2019-10-27 12:37:59 11.18676 11.22743 7.723114 9.461530
## 103964: M03 2019-10-27 12:39:59 11.19001 11.23277 7.722659 9.466332
## 103965: M03 2019-10-27 12:42:00 11.19109 11.25243 7.721918 9.470595
## 103966: M03 2019-10-27 12:43:59 11.19839 11.26209 7.723172 9.473182
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 103962: 28.8
## 103963: 28.8
## 103964: 28.8
## 103965: 28.8
## 103966: 28.8
##
## [[8]]
## moats dateTime aTemperature sTemperature pH
## 1: M04 2019-10-05 14:03:00 11.91434 12.44716 8.825669
## 2: M04 2019-10-05 14:03:23 11.91526 12.44246 8.826660
## 3: M04 2019-10-05 14:03:47 11.91462 12.44424 8.830653
## 4: M04 2019-10-05 14:04:12 11.91659 12.44531 8.830290
## 5: M04 2019-10-05 14:04:35 11.91764 12.44556 8.835112
## ---
## 100737: M04 2019-10-18 15:07:00 -273.04429 -273.22433 8123.273242
## 100738: M04 2019-10-18 15:08:59 -273.04429 -273.22433 8123.273242
## 100739: M04 2019-10-18 15:11:00 -273.04429 -273.22433 8123.273242
## 100740: M04 2019-10-18 15:13:00 -273.04429 -273.22433 8123.273242
## 100741: M04 2019-10-18 15:15:00 -273.04429 -273.22433 8123.273242
## DO salinity
## 1: 9.024436 28.8
## 2: 9.024051 28.8
## 3: 9.023525 28.8
## 4: 9.023639 28.8
## 5: 9.023378 28.8
## ---
## 100737: -2.070290 28.8
## 100738: -2.070290 28.8
## 100739: -2.070290 28.8
## 100740: -2.070290 28.8
## 100741: -2.070290 28.8
##
## [[9]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M05 2019-09-24 10:04:00 13.45163 13.38484 7.604477 4.430357
## 2: M05 2019-09-24 10:04:23 13.45253 13.38669 7.604743 4.428692
## 3: M05 2019-09-24 10:04:47 13.46313 13.38174 7.604795 4.424452
## 4: M05 2019-09-24 10:05:12 13.46482 13.38264 7.605025 4.418166
## 5: M05 2019-09-24 10:05:35 13.47658 13.38145 7.604209 4.411646
## ---
## 69881: M05 2019-10-19 09:54:00 -12.13223 13.69912 8.720219 7.927978
## 69882: M05 2019-10-19 09:56:00 -12.13115 13.67656 8.721747 7.929497
## 69883: M05 2019-10-19 09:58:00 -12.12418 13.65593 8.724045 7.927378
## 69884: M05 2019-10-19 10:00:00 -12.19022 13.65425 8.726931 7.933592
## 69885: M05 2019-10-19 10:01:59 -12.19581 13.63223 8.726680 7.937080
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 69881: 28.8
## 69882: 28.8
## 69883: 28.8
## 69884: 28.8
## 69885: 28.8
##
## [[10]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M06 2019-09-24 16:42:00 13.97340 13.59691 7.733539 8.457947
## 2: M06 2019-09-24 16:42:23 13.97440 13.59848 7.732976 8.467310
## 3: M06 2019-09-24 16:42:47 13.97289 13.59922 7.732138 8.476004
## 4: M06 2019-09-24 16:43:11 13.97367 13.59896 7.731699 8.485997
## 5: M06 2019-09-24 16:43:35 13.97104 13.59855 7.731255 8.495649
## ---
## 82681: M06 2019-10-23 16:32:59 14.09326 14.18709 7.713069 8.819638
## 82682: M06 2019-10-23 16:35:00 14.06091 14.20211 7.712702 8.793442
## 82683: M06 2019-10-23 16:37:00 14.06299 14.27481 7.711946 8.767664
## 82684: M06 2019-10-23 16:39:00 14.07089 14.31402 7.711172 8.739593
## 82685: M06 2019-10-23 16:40:59 14.07484 14.32679 7.710819 8.709578
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 82681: 28.8
## 82682: 28.8
## 82683: 28.8
## 82684: 28.8
## 82685: 28.8
##
## [[11]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M07 2019-09-24 16:07:00 12.13279 10.80555 7.714717 8.967500
## 2: M07 2019-09-24 16:07:24 12.14024 10.77122 7.714582 8.973155
## 3: M07 2019-09-24 16:07:48 12.12668 10.74974 7.713819 8.978636
## 4: M07 2019-09-24 16:08:12 12.12292 10.73651 7.714620 8.983874
## 5: M07 2019-09-24 16:08:36 12.12922 10.71033 7.714409 8.989965
## ---
## 51418: M07 2019-10-23 11:18:22 11.92310 12.44842 7.700380 7.728110
## 51419: M07 2019-10-23 11:20:22 11.93719 12.52979 7.698907 7.824580
## 51420: M07 2019-10-23 11:22:22 11.94029 12.61646 7.694582 7.900057
## 51421: M07 2019-10-23 11:24:22 11.95217 12.68966 7.697589 7.958912
## 51422: M07 2019-10-23 11:26:22 12.02391 12.75254 7.696608 8.004696
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 51418: 28.8
## 51419: 28.8
## 51420: 28.8
## 51421: 28.8
## 51422: 28.8
##
## [[12]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M08 2019-09-24 16:19:00 13.97487 13.65684 7.532832 8.589048
## 2: M08 2019-09-24 16:19:24 13.97642 13.65925 7.534100 8.589045
## 3: M08 2019-09-24 16:19:47 13.98313 13.65838 7.534952 8.589651
## 4: M08 2019-09-24 16:20:12 13.98327 13.66222 7.536382 8.590224
## 5: M08 2019-09-24 16:20:36 13.97546 13.67105 7.538302 8.590668
## ---
## 82819: M08 2019-10-20 16:10:00 13.78250 13.90584 7.566084 8.552211
## 82820: M08 2019-10-20 16:12:00 13.78034 13.97506 7.572273 8.547564
## 82821: M08 2019-10-20 16:14:00 13.78263 14.07372 7.577503 8.541789
## 82822: M08 2019-10-20 16:16:00 13.77039 14.19781 7.579950 8.534088
## 82823: M08 2019-10-20 16:17:59 13.77569 14.30966 7.577583 8.526458
## salinity
## 1: 28.8
## 2: 28.8
## 3: 28.8
## 4: 28.8
## 5: 28.8
## ---
## 82819: 28.8
## 82820: 28.8
## 82821: 28.8
## 82822: 28.8
## 82823: 28.8
##
## [[13]]
## moats dateTime aTemperature sTemperature pH DO
## 1: M09 2019-09-26 14:51:00 11.08368 10.68647 1.011406 8.890546
## 2: M09 2019-09-26 14:51:24 11.07862 10.67480 1.011406 8.889532
## 3: M09 2019-09-26 14:51:47 11.07612 10.66337 1.011406 8.890120
## 4: M09 2019-09-26 14:52:12 11.07909 10.63342 1.011406 8.891280
## 5: M09 2019-09-26 14:52:35 11.08047 10.60913 1.011406 8.893174
## ---
## 99685: M09 2019-09-28 14:48:59 10.99281 10.91662 1.011406 8.747725
## 99686: M09 2019-09-28 14:49:24 10.97035 10.88477 1.011406 8.743497
## 99687: M09 2019-09-28 14:49:47 10.95868 10.85422 1.011406 8.740896
## 99688: M09 2019-09-28 14:50:12 10.95436 10.81981 1.011406 8.739749
## 99689: M09 2019-09-28 14:50:36 10.98239 10.78134 1.011406 8.741034
## salinity
## 1: 29.5
## 2: 29.5
## 3: 29.5
## 4: 29.5
## 5: 29.5
## ---
## 99685: 29.5
## 99686: 29.5
## 99687: 29.5
## 99688: 29.5
## 99689: 29.52.3 Create a new vector for Moats data logs
## 2.3 Create a new vector for Moats data logs
# "M01thruM13Moatslog_data" via rbind
M01thruM13moatslog_data <- rbindlist(temp)
print(M01thruM13moatslog_data)## moats dateTime aTemperature sTemperature pH
## 1: M01 2019-09-05 17:04:00 12.16840 11.32412 0.02244800
## 2: M01 2019-09-05 17:04:22 12.17155 11.34600 0.03559150
## 3: M01 2019-09-05 17:04:46 12.17656 11.36786 0.02263175
## 4: M01 2019-09-05 17:05:10 12.17969 11.39616 0.04348125
## 5: M01 2019-09-05 17:05:34 12.18132 11.43941 0.02432750
## ---
## 1137876: M09 2019-09-28 14:48:59 10.99281 10.91662 1.01140600
## 1137877: M09 2019-09-28 14:49:24 10.97035 10.88477 1.01140600
## 1137878: M09 2019-09-28 14:49:47 10.95868 10.85422 1.01140600
## 1137879: M09 2019-09-28 14:50:12 10.95436 10.81981 1.01140600
## 1137880: M09 2019-09-28 14:50:36 10.98239 10.78134 1.01140600
## DO salinity
## 1: 9.269025 29.5
## 2: 9.268738 29.5
## 3: 9.268492 29.5
## 4: 9.267310 29.5
## 5: 9.266493 29.5
## ---
## 1137876: 8.747725 29.5
## 1137877: 8.743497 29.5
## 1137878: 8.740896 29.5
## 1137879: 8.739749 29.5
## 1137880: 8.741034 29.52.4 Write the new csv document
## 2.5 Write the new csv document |
# "M01thruM13moatslog" can uncomment this command line as needed
#write.csv(M01thruM13moatslog_data, file = "M01thruM13moatslog.csv", row.names = FALSE)Saved on the OA Google Drive M01thruM13moatslog.csv
3.) Creating the Dataframe “dml”
3.1 Reading the CSV
## 3.1 Reading the CSV |
## ensuring column names and types
## Data Moats Log = dml
dml <- read.csv( file = "M01thruM13moatslog.csv", stringsAsFactors = FALSE)
dim(dml)## [1] 1137880 73.1a Duplication Check
## 3.1a Duplication Check
## duplicates observed in "dml" on 2020.05.07
dup2 <- dml[duplicated(dml),]
#if no dups, Cdml2 has same number of rows as Cdml
dml2 <- dml %>% distinct() 3.1b Sub sampling dataframe “dml”
## 3.1b Sub sampling dataframe "dml"
## creating a sub sample of the data moats log dml dataframe to allow for quick graphs
#subsample every 17th row (because prime numbers are indeed cool)
dml <- dml %>% arrange(moats, dateTime) %>% filter(row_number() %% 17 == 0)
#write.csv(dml, file = "M01thruM13moatslog_n17.csv", row.names = FALSE)3.2 Checking variables
## 3.2 Checking variables
## Looking to ensure the different variables are treated as the correct variable type
## Checking the names in the dataframe
names(dml)## [1] "moats" "dateTime" "aTemperature" "sTemperature" "pH"
## [6] "DO" "salinity"## Checking variable type/class
class(dml$moats)## [1] "character"dml$moats <- as.factor(dml$moats)3.3 Changing variables
## 3.3 Changing variables |
## Changing MOATs to Factors for the 13 different MOATs- these will be the discrete units for follow analysis
dml$moats <- factor(dml$moats)
# Checking the names of the different levels
levels(dml$moats)## [1] "M01" "M02" "M03" "M04" "M05" "M06" "M07" "M08" "M09" "M10" "M11" "M12"
## [13] "M13"##checking the dataset, dimensions
dim(dml)## [1] 66934 7#4.) Creating dateTime objects
4.0 Establish the dateTime objects
# 4.0 establish the date time object of the CSV |
dml$dateTime <- as.POSIXct(dml$dateTime, format="%Y-%m-%d %H:%M:%OS")
ReferenceTime <- as.POSIXct("2019-09-20 23:59:00")
class(ReferenceTime)## [1] "POSIXct" "POSIXt"# QA check
dim(dml)## [1] 66934 75.) Creating Treatment Variables
5.1 Identifying Treatments by MOATS
## 5.1 Identifying treatments by moats
## establishing treatments
dml$treatment <- ""
dml$treatment[dml$moats == "M07" | dml$moats== "M10" | dml$moats== "M12"] <- "current"
dml$treatment[dml$moats == "M01"| dml$moats== "M06"] <- "hightemperature"
dml$treatment[dml$moats == "M02"| dml$moats== "M08" | dml$moats== "M13"] <- "allchange"
dml$treatment[dml$moats == "M03"| dml$moats == "M04" | dml$moats == "M05" | dml$moats== "M11"] <- "broken_and_ambientbroken"
#verify that this new column has been created
names(dml)## [1] "moats" "dateTime" "aTemperature" "sTemperature" "pH"
## [6] "DO" "salinity" "treatment"#results should include:
#[1] "moats" "dateTime" "aTemperature" "sTemperature" "pH"
#[6] "DO" "salinity" "treatment"
# QA check
dim(dml)## [1] 66934 85.2 Simple ggplot to show the different MOATs without polish
plot5.2 <- ggplot(dml, aes(x=dateTime, y=aTemperature)) +
geom_point(aes(colour=moats, point=)) +
ggtitle("All MOATs, All Treatment aTemperature Time Series")
plot5.2
Plot 5.2 Figure1
6.) Creating Night and Day Periods
6.1 Narrative (Overall) Creating a day and night variables Day and night periods exclude the acclimation period by their definition. Day and night changed at about ~ 1230 on 05OCT19 Treatment start date considered to begin Monday 23SEP19 at 1200pm
Krill Night Started 1200 (~1230) and ends 2100 Krill Days Started 2101 and ends 1159 (~1229)
Interval 1 start 1200 23SEP19, end 1229 05OCT19
Interval 2 start 1230 05OCT19, end 2100 30OCT19
Concept Diagram graphic
Day/Night Definitions Concept Diagram Figure2
6.2 New Column, New Variable
## 6.2 New Column, New Variable in dml
#creating a new column, new variable "period"
dml$period <- ""6.3 Disassembling dateTime to create 2 new variables
## 6.3 Disassembling dateTime to create 2 new variables
# Create new split date and time columns
dml$ObservationDate <- as.Date(dml$dateTime)
dml$ObservationTime <- format(as.POSIXct(dml$dateTime) ,format = "%H:%M:%S")6.4 Narrative about Intervals
Interval 1
- Interval Date Start “2019-09-23”
- Interval Date End “2019-10-05”
- Day Start Time “21:01:00”
- Day End Time “12:01:00”
- Night Start Time “12:00:00”
- Night End Time “21:00:00”
- Other Time
Interval 2
- Interval Date Start “2019-10-05”
- Interval Date End “2019-10-30”
- Day Start Time “21:01:00”
- Day End Time “12:29:00”
- Night Start Time “12:30:00”
- Night End Time “21:00:00”
- Other Time
6.5 Day / Night Assignments
## 6.5 Day / Night Assignments
# Using the "case_when" function in the tidyverse in the place of a loop
dml <- dml %>% mutate(period=case_when(
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& (ObservationTime >= "12:00:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& (ObservationTime >= "12:30:00")
& (ObservationTime <="21:00:00") ~"night",
(ObservationDate >= "2019-09-23")
& (ObservationDate <="2019-10-05")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <="11:59:00")) ~"day",
(ObservationDate >= "2019-10-05")
& (ObservationDate <= "2019-10-30")
& ((ObservationTime >= "21:01:00")
| (ObservationTime <= "12:29:00")) ~"day",
TRUE ~"other"
)
) 7.) Insitu data
7.0 In-situ data files Two files represent manual checks of aquarium salinity. Extracted from this are point measurements of aquarim temperature
# ## 7.1 Read in files
# # carefule about navigating to the second working directory
#
#
# knitr::opts_chunk$set(echo = TRUE)
# knitr::opts_knit$set(root.dir = "/Users/katherinerovinski/GIT/NWFSC.MUK_KRL_SMR2019/06. MOATS replication verification/01. Raw Data/InSituSampling")
#
#
# d.insitu <- read.csv(file = "KRL19_insitu_sample_Day.csv", stringsAsFactors = FALSE)
# n.insitu <- read.csv(file = "KRL19_insitu_sample_Night.csv", stringsAsFactors = FALSE)8.) Cleaning up dml
Creating a new dataframe cleaned with the various filters below checking on the variables inside dml Cleaned-Up dml = Cdml
## 8.1 Noting names of the different variables in dml
names(dml)## [1] "moats" "dateTime" "aTemperature" "sTemperature"
## [5] "pH" "DO" "salinity" "treatment"
## [9] "period" "ObservationDate" "ObservationTime"8.2 Narrative on desired changes to create Cdml
8.2 Narrative on Cdml Temperatures below 5C and above 30C are thought to be less than probable
# Changes to be made the dataframe by variable
#[1] "moats" "M03", "M04", "M05", "M09", "M11" to be filtered out- all these
# MOATs were dropped from the study
#[2] "dateTime" - no changes
#[3] "aTemperature" - no changes
#[4] "sTemperature" - no changes
#[5] "pH" - no changes
#[6] "DO"- no changes
#[7] "salinity" - no changes
#[8] "treatment" - dropping the listed MOATs will eliminate the
# "broken_and_ambientbroken" treatment
#[9] "period" - filtering out "other"
#[10] "ObservationDate" - no changes
#[11] "ObservationTime" - no changes, note that each observation could be
# spaced 6.8minutes apart
Cdml <- dml %>% filter(!moats %in% c("M03", "M04", "M05", "M11")) %>%
filter(aTemperature>= 5 & aTemperature<=30) %>%
filter(treatment %in% c("current", "allchange", "hightemperature")) %>%
filter(period != "other")8.3 Option to write a CSV of Cdml parameters
droping the levels of the moats
levels(Cdml$moats)## [1] "M01" "M02" "M03" "M04" "M05" "M06" "M07" "M08" "M09" "M10" "M11" "M12"
## [13] "M13"Cdml$moats <- droplevels(Cdml$moats)9.) Framing filters for Cdml
9.0 cleaning names of levels, factors
## 9.0 Removing the names of moats & treatments removed from Cdml
# Removal should allow for cleaner graphs
# Determined moats lab never got under 5C and was never over 30C
filteredFrame = filter(Cdml,
!moats %in% c('M03', "M04", "M05", "M11") &
(aTemperature>= 5 & aTemperature<=30) &
treatment %in% c("current", "allchange", "hightemperature") &
period != "other")Without dropping levels and factors not named label may still crowd plots #### 9.1 Dropping names and factors
## 9.1 Dropping levels and factors
filteredFrame$moats <- droplevels(filteredFrame$moats)
filteredFrame$treatment <- factor(filteredFrame$treatment)9.2 Broad-gague test of plots by treatment
9.2.1 All Change Day Period Boxplot
simpleplot_allchg_day <- ggplot(subset(Cdml[Cdml$treatment == "allchange", ],
period %in% ("day")),
aes(x=moats, y=aTemperature,
colour=treatment)) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
ggtitle("All Change Treatment, Aquarium Temperature,Day Period Boxplot")
simpleplot_allchg_day
9.2.2 All Change Night Period Boxplot
simpleplot_allchg_night <- ggplot(subset(Cdml[Cdml$treatment == "allchange", ],
period %in% ("night")),
aes(x=moats, y=aTemperature,
colour=treatment)) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
ggtitle("All Change Treatment, Aquarium Temperature, Night Period Boxplot")
simpleplot_allchg_night
9.2.3 All Change Timeseries
simpleplot_allchg_timeseries <- ggplot(subset(Cdml[Cdml$treatment == "allchange", ]))+
aes(x=dateTime, y=aTemperature) +
geom_point(aes(colour=moats, point=)) +
ggtitle("All Change Treatment, Aquarium Temperature, Timeseries")
simpleplot_allchg_timeseries
9.2.4 Current Day Period Boxplot
simpleplot_cur_day <- ggplot(subset(Cdml[Cdml$treatment == "current", ],
period %in% ("day")),
aes(x=moats, y=aTemperature,
colour=treatment)) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
ggtitle("Current Conditions Treatment, Aquarium Temperature, Day Period Boxplot")
simpleplot_cur_day
9.2.5 Current Night Period Boxplot
simpleplot_cur_night <- ggplot(subset(Cdml[Cdml$treatment == "current", ],
period %in% ("night")),
aes(x=moats, y=aTemperature,
colour=treatment)) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
ggtitle("Current Conditions Treatment, Aquarium Temperature, Night Period Boxplot")
simpleplot_cur_night
9.2.6 Current Conditions Timeseries
simpleplot_cur_timeseries <- ggplot(subset(Cdml[Cdml$treatment == "current", ]))+
aes(x=dateTime, y=aTemperature) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Current Conditions Treatment, Aquarium Temperature, Timeseries")
simpleplot_cur_timeseries
9.2.7 High Temperature Day Period Boxplot
simpleplot_hitemp_day <- ggplot(subset(Cdml[Cdml$treatment == "hightemperature", ],
period %in% ("day")),
aes(x=moats, y=aTemperature,
colour=treatment)) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
ggtitle("High Temperature, Aquarium Temperature, Conditions Day Period Boxplot")
simpleplot_hitemp_day
9.2.8 High Temperature Night Period Boxplot
simpleplot_hitemp_night <- ggplot(subset(Cdml[Cdml$treatment == "hightemperature", ],
period %in% ("night")),
aes(x=moats, y=aTemperature,
colour=treatment)) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
ggtitle("High Temperature Conditions, Aquarium Temperature, Night Period Boxplot")
simpleplot_hitemp_night
9.2.9 High Temperature Timeseries
simpleplot_cur_timeseries <- ggplot(subset(Cdml[Cdml$treatment == "current", ]))+
aes(x=dateTime, y=aTemperature) +
geom_point(aes(colour=moats, point=)) +
ggtitle("Current Conditions Aquarium Temperature Treatment Timeseries")
simpleplot_cur_timeseries
10.) Temperature Jumps
10.0 Creating new column
## 10.0 Creating new column - showing the different between 2 adjacent times
# first sort the data so you are comparing adjacent times
# then add new column
# the diff() function computes x[i+lag] - x[i], the default value is lag = 110.1 diff()function & deltaTempLag1 Narrative
## 10.1 diff()function & deltaTempLag1 Narrative
#the result of the diff() function has a length that is the length of
# the original vector - lag
#therefore need to fill the new variable, deltaTempLag1,
# with c(0,abs(diff(aTemperature))) it is right length and first value
# (with no valid diff) is zero
# creating a new object to be a validation flag under name deltaTempLag1
# will create a numerical value that will represent those observations
# to be filtered out10.2 Creating deltaTempLag1
## 10.2 Creating deltaTempLag1
Cdml <- filteredFrame %>% arrange(moats, dateTime)%>%
mutate(deltaTempLag1 = c(0,abs(diff(aTemperature))))
#the diff value comparing the last time in M01 to the first time in M02 (etc.)
# is not valid,
# Set those to zero
Cdml$deltaTempLag1[lag(Cdml$moats) != Cdml$moats] <- 0
#this shows all the rows that jump more that 1 degree from previous row
# (ingoring rows that are transition from one moats to another)
Cdml %>% filter(deltaTempLag1 >1)## moats dateTime aTemperature sTemperature pH DO
## 1 M01 2019-10-19 21:59:00 11.71136 11.67883 7.722845 5.179468
## 2 M07 2019-09-26 21:43:00 11.39582 10.83490 7.689979 5.656842
## 3 M08 2019-09-30 21:43:59 13.23410 12.25973 7.548849 5.518189
## 4 M08 2019-10-19 21:19:59 12.95044 12.85344 7.556915 5.804039
## 5 M08 2019-10-24 21:27:59 13.19683 13.42282 7.559878 6.215845
## 6 M08 2019-10-25 21:15:59 12.43890 11.90826 7.586635 6.300650
## 7 M08 2019-10-28 21:47:59 13.33322 13.46518 7.534707 4.086717
## 8 M10 2019-09-26 21:44:59 11.41436 10.86563 7.691018 7.202747
## 9 M12 2019-09-29 21:41:59 11.38074 10.89259 7.690470 5.511001
## 10 M12 2019-10-28 21:33:59 11.28371 11.55635 7.713491 7.449388
## 11 M13 2019-10-09 21:32:59 12.70052 12.62047 7.558021 5.813056
## salinity treatment period ObservationDate ObservationTime
## 1 28.8 hightemperature day 2019-10-20 21:59:00
## 2 28.8 current day 2019-09-27 21:43:00
## 3 28.8 allchange day 2019-10-01 21:43:59
## 4 28.8 allchange day 2019-10-20 21:19:59
## 5 28.8 allchange day 2019-10-25 21:27:59
## 6 28.8 allchange day 2019-10-26 21:15:59
## 7 28.8 allchange day 2019-10-29 21:47:59
## 8 28.8 current day 2019-09-27 21:44:59
## 9 28.8 current day 2019-09-30 21:41:59
## 10 28.8 current day 2019-10-29 21:33:59
## 11 28.8 allchange day 2019-10-10 21:32:59
## deltaTempLag1
## 1 1.386639
## 2 1.083771
## 3 1.063837
## 4 1.090132
## 5 1.046439
## 6 1.828905
## 7 1.206709
## 8 1.020578
## 9 1.029939
## 10 1.081021
## 11 1.12232210.3 Creating tDeltaThreshold
```{r10.3 Creating tDeltaThreshold}
10.3 Creating tDeltaThreshold
#Creating another column of variables to be able to graph the temperature jumps Cdml <- Cdml %>% mutate(tDeltaThreshold = if_else(deltaTempLag1 > 0.5, TRUE, FALSE)) ```
11.) Calculating Averages by Treatment & Day/Night
## 11.0 Intercept Narrative
# These averages will be the yintercepts in plots
# example of what to put into with ggplot "
# geom_hline(yintercept = dtemperatur$`mean(aTemperature)`) 11.1 All Treatments (Day & Night)
## 11.1 All Treatments (Day & Night)
# Night Period
subsetNightaTemp <- subset(filteredFrame, period == "night" & aTemperature >0,
select = c( moats, dateTime, treatment, aTemperature ))
# Day Period
subsetDayaTemp <- subset(filteredFrame, period == "day" & aTemperature >0,
select = c( moats, dateTime, treatment, aTemperature ))11.2 Current Treatment Day/Night Averages
## 11.2 Current Treatment
# Current Night
curNightaTemp <- subset(Cdml, period == "night" & treatment == "current",
select = c(dateTime, aTemperature ))
avg_curNightaTemp <- mean(curNightaTemp$aTemperature)
print(avg_curNightaTemp)## [1] 11.95039# [1] 11.95122
# Current Day
curDayaTemp <- subset(Cdml, period == "day" & treatment == "current",
select = c(dateTime, aTemperature ))
avg_curDayaTemp <- mean(curDayaTemp$aTemperature)
print(avg_curDayaTemp)## [1] 11.09783# [1] 11.0998311.3 All Change Treatment Day/Night Averages
## 11.3 All Change Treatment
# All Change Night
allchgNightaTemp <- subset(Cdml, period == "night" & treatment == "allchange",
select = c(dateTime, aTemperature ))
avg_allchgNightaTemp <- mean(allchgNightaTemp$aTemperature)
print(avg_allchgNightaTemp)## [1] 13.89858# [1] 13.89847
# All Change Day
allchgDayaTemp <- subset(Cdml, period == "day" & treatment == "allchange",
select = c(dateTime, aTemperature ))
avg_allchgDayaTemp <- mean(allchgDayaTemp$aTemperature)
print(avg_allchgDayaTemp)## [1] 12.97331# [1] 12.9746611.4 High Temperature Treatment
# High Temperature Conditions
# High Temperature Night
hitempNightaTemp <- subset(Cdml, period == "night" & treatment == "hightemperature",
select = c(dateTime, aTemperature ))
avg_hitempNightaTemp <- mean(hitempNightaTemp$aTemperature)
print(avg_hitempNightaTemp)## [1] 13.80435#[1] 13.80397
# High Temperature Day
hitempDayaTemp <- subset(Cdml, period == "day" & treatment == "hightemperature",
select = c(dateTime, aTemperature ))
avg_hitempDayaTemp <- mean(hitempDayaTemp$aTemperature)
print(avg_hitempDayaTemp)## [1] 12.92259#[1] 12.9214811.5 Review of all Treatments
## 11.5 Review of all Treatments
# Review of 6 new variables
avg_allchgDayaTemp## [1] 12.97331avg_allchgNightaTemp## [1] 13.89858avg_curDayaTemp## [1] 11.09783avg_curNightaTemp## [1] 11.95039avg_hitempDayaTemp## [1] 12.92259avg_hitempNightaTemp## [1] 13.8043511.6 Table of Averages
## 11.6 Table of Averages
meanCdmldatemp <- filteredFrame %>% group_by(treatment, period) %>%
summarise(mean(aTemperature))
# Groups: treatment [3]
#treatment period `mean(aTemperature)`
#<chr> <chr> <dbl>
# 1 allchange day 13.0
# 2 allchange night 13.9
# 3 current day 11.1
# 4 current night 12.0
# 5 hightemperature day 12.9
# 6 hightemperature night 13.8
print(meanCdmldatemp)## # A tibble: 6 x 3
## # Groups: treatment [3]
## treatment period `mean(aTemperature)`
## <fct> <chr> <dbl>
## 1 allchange day 13.0
## 2 allchange night 13.9
## 3 current day 11.1
## 4 current night 12.0
## 5 hightemperature day 12.9
## 6 hightemperature night 13.812.) Summary & Group by Cdml
# 12.0 Cdml Day Summary, group by, mutate
Cdml.daynight.summary <- Cdml %>% group_by(treatment, period) %>%
summarize(sd = sd(aTemperature, na.rm = TRUE),
mean = mean(aTemperature, na.rm = TRUE),
median = median(aTemperature, na.rm = TRUE),
IQR = IQR(aTemperature, na.rm = TRUE),
n = n()) %>%
mutate(se = sd/sqrt(n)) %>%
mutate(ci = se*1.96)
write.csv(Cdml.daynight.summary, "2020.07.08_Cdml_daynight_summary.csv")12.1 Tribble of Day/Night Summary
tibble::tribble(
~treatment, ~period, ~sd, ~mean, ~median, ~IQR, ~n, ~se, ~ci,
"allchange", "day", 0.372506855, 12.97330841, 12.896611, 0.24780225, 4956L, 0.005291376, 0.010371097,
"allchange", "night", 0.247588899, 13.89858376, 13.92320525, 0.193622875, 2843L, 0.004643471, 0.009101203,
"current", "day", 0.348206031, 11.09782575, 11.07660075, 0.207690563, 3974L, 0.005523602, 0.010826259,
"current", "night", 0.302526369, 11.95038837, 12.0529935, 0.122263063, 2290L, 0.006321869, 0.012390863,
"hightemperature", "day", 0.362913386, 12.92259015, 12.9101975, 0.288474875, 2687L, 0.007001146, 0.013722247,
"hightemperature", "night", 0.313433995, 13.80435016, 13.87954738, 0.188507875, 1504L, 0.008082062, 0.015840842
)## # A tibble: 6 x 9
## treatment period sd mean median IQR n se ci
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 allchange day 0.373 13.0 12.9 0.248 4956 0.00529 0.0104
## 2 allchange night 0.248 13.9 13.9 0.194 2843 0.00464 0.00910
## 3 current day 0.348 11.1 11.1 0.208 3974 0.00552 0.0108
## 4 current night 0.303 12.0 12.1 0.122 2290 0.00632 0.0124
## 5 hightemperature day 0.363 12.9 12.9 0.288 2687 0.00700 0.0137
## 6 hightemperature night 0.313 13.8 13.9 0.189 1504 0.00808 0.015813.) Plots
## 13.1 Boxplot aTemp by moats
#boxplot(aTemperature~moats, Cdml)
ggplot(Cdml, aes(treatment, aTemperature)) +
geom_jitter(color = "grey") +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = Cdml.daynight.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data = Cdml.daynight.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = Cdml.daynight.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
facet_wrap(~period) +
ggtitle("All Treatments, Day/Night Period Aquarium Temperature Comparison") +
theme_bw() 
## 13.1a Boxplot aTemp by moats
#boxplot(aTemperature~moats, Cdml)
ggplot(Cdml, aes(treatment, aTemperature)) +
geom_jitter(color = "grey") +
geom_boxplot(notch = TRUE, outlier.shape = NA, colour = "green") +
geom_point(data = Cdml.daynight.summary, aes(x=treatment, y=mean), size=5, color = "purple") +
geom_errorbar(data = Cdml.daynight.summary,
aes(x=treatment, y=mean, ymin = mean-sd, ymax = mean+sd),
color = "blue") +
geom_errorbar(data = Cdml.daynight.summary,
aes(x=treatment, y=mean, ymin = mean-ci, ymax = mean+ci),
colour = "red") +
facet_wrap(~period) +
ggtitle("All Treatments, ay/Night Period Aquarium Temperature Comparison, Temperature Jumps at .5C") +
theme_bw() 
13.1.1 Aquarium Temperature, All Change Conditions, Day & Night
p_allchg_facetwrap <- ggplot(subset(Cdml[Cdml$treatment == "allchange", ])) +
aes(x=moats, y=aTemperature,
colour=treatment) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
facet_wrap(~period) +
ggtitle("All Change Conditions, Aquarium Temperature, Day/Night Period Boxplots")
p_allchg_facetwrap 
p_allchg_facetwrap_vio <- ggplot(subset(Cdml[Cdml$treatment == "allchange", ])) +
aes(x=moats, y=aTemperature,
colour=treatment) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_violin() +
facet_wrap(~period) +
ggtitle("All Change Conditions, Aquarium Temperature, Day/Night Period Violin Plots")
p_allchg_facetwrap_vio 
13.1.3 Aquarium Temperature, Current Conditions, Day & Night
p_cur_facetwrap_box <- ggplot(subset(Cdml[Cdml$treatment == "current", ])) +
aes(x=moats, y=aTemperature,
colour=treatment) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
facet_wrap(~period) +
ggtitle("Current Conditions, Aquarium Temperature, Day/Night Period Boxplots")
p_cur_facetwrap_box
p_allchg_facetwrap_vio <- ggplot(subset(Cdml[Cdml$treatment == "current", ])) +
aes(x=moats, y=aTemperature,
colour=treatment) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_violin() +
facet_wrap(~period) +
ggtitle("Current Conditions, Aquarium Temperature, Day/Night Period Violin Plots")
p_allchg_facetwrap_vio 
13.1.4 Aquarium Temperature, High Temperature Conditions, Day & Night Boxplots
p_hitemp_facetwrap_box <- ggplot(subset(Cdml[Cdml$treatment == "hightemperature", ])) +
aes(x=moats, y=aTemperature,
colour=treatment) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_boxplot() +
facet_wrap(~period) +
ggtitle("High Temperature Conditions, Aquarium Temperature, Day/Night Period Boxplots")
p_hitemp_facetwrap_box
13.1.5 Aquarium Temperature, High Temperature Conditions, Day & Night Violin plots
p_hitemp_facetwrap_vio <- ggplot(subset(Cdml[Cdml$treatment == "hightemperature", ])) +
aes(x=moats, y=aTemperature,
colour=treatment) +
stat_summary(fun=mean, geom="point",
size=2, color="red") +
geom_violin() +
facet_wrap(~period) +
ggtitle("High Temperature Conditions, Aquarium Temperature, Day/Night Period Violin Plots")
p_hitemp_facetwrap_vio 
14. Cumulative Frequency Distribution
A Cumulative Frequency Distribution plot was used to illustration the time spent around the mean per period per treatment.
14.1a CFD plot 1
CFD1 <- ggplot(Cdml, aes(aTemperature)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = c(11,12,13,14), colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
CFD1
14.2 Empirical Cumulative Distribution Function Generated Plots
Using a ECDF to better visualize distribution Compute empirical cumulative distribution : The empirical cumulative distribution function (ECDF) provides an alternative visualisation of distribution. Compared to other visualisations that rely on density (like geom_histogram()), the ECDF doesn’t require any tuning parameters and handles both continuous and categorical variables.
## 14.2a ECDF Plot
ECDFplot <- ggplot(Cdml, aes(aTemperature)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = Cdml.daynight.summary$mean, colour = "red") +
facet_wrap(vars(treatment, period), ncol = 2, scales = "fixed") +
theme_bw()
ECDFplot
14.3 Creating a function to incorporate our statisitics
## 14.3 Creating a function to incorporate our statisitics- show our averages
coolECDF <- function(d, dsum, treat, per){
dsum <- dsum %>% filter(treatment == treat & period == per )
p <- d %>% filter(treatment == treat & period == per ) %>% ggplot(aes(aTemperature)) +
stat_ecdf(aes(colour = moats)) +
geom_vline(xintercept = dsum$mean, colour = "red") +
ggtitle(paste(treat, per)) +
theme_bw()
return(p)
}14.4 Levels ECDF
levels(Cdml$treatment)## [1] "allchange" "current" "hightemperature" levels(factor(Cdml$period))## [1] "day" "night"14.5 Creating the different averages from the Summary Table (ECDF)
cfd.a.d <- coolECDF(Cdml, Cdml.daynight.summary, "allchange", "day")
cfd.a.n <- coolECDF(Cdml, Cdml.daynight.summary, "allchange", "night")
cfd.c.d <- coolECDF(Cdml, Cdml.daynight.summary, "current", "day")
cfd.c.n <- coolECDF(Cdml, Cdml.daynight.summary, "current", "night")
cfd.h.d <- coolECDF(Cdml, Cdml.daynight.summary, "hightemperature", "day")
cfd.h.n <- coolECDF(Cdml, Cdml.daynight.summary, "hightemperature", "night")14.6 Incorporating Grid Arrange to display the ECDF plots
## Incorporating Grid Arrange to display the ECDF plots
# Basic Grid Arrange
grid.arrange(cfd.a.d, cfd.c.d, cfd.h.d, nrow = 1)
grid.arrange(cfd.a.n, cfd.c.n, cfd.h.n, nrow = 1) 
#make a list of all the plots then pass the list to grid.arrange()
ecdfList <- list(cfd.a.d, cfd.c.d, cfd.h.d, cfd.a.n, cfd.c.n, cfd.h.n)
grid.arrange(grobs = ecdfList, ncol=3)
15.0 Temperature Investigation
15.1 Temperature Investigation High Temperature
# Selecting for the high aTemperature Band
investig.HighaTemp <- Cdml %>%
filter(aTemperature>= 14.50 & aTemperature<=15.50) %>%
filter(treatment %in% c("current",
"allchange",
"hightemperature")) %>%
filter(period != "other")15.1a High Temperature Banding Investigation, All Change Treatment
hitemp_plot1 <- ggplot(subset(investig.HighaTemp[investig.HighaTemp$treatment == "allchange", ])) +
aes(x=dateTime, y=aTemperature) +
geom_point(aes(colour=moats, point=)) +
ggtitle("High Temperature Banding Investigation, All Change Treatment")
ylim (10, 20)## <ScaleContinuousPosition>
## Range:
## Limits: 10 -- 20hitemp_plot1
15.1b High Temperature Banding Investigation, Current Conditions Treatment
hitemp_plot2 <- ggplot(subset(investig.HighaTemp[investig.HighaTemp$treatment == "current", ])) +
aes(x=dateTime, y=aTemperature) +
geom_point(aes(colour=moats, point=)) +
ggtitle("High Temperature Banding Investigation, Current Conditions Treatment")
ylim (10, 20)## <ScaleContinuousPosition>
## Range:
## Limits: 10 -- 20hitemp_plot2
15.1c High Temperature Banding Investigation, High Temperature Treatment Conditions Treatment
```{r15.1b High Temperature Banding Investigation, High Temperature Treatment Treatment}
hitemp_plot3 <- ggplot(subset(investig.HighaTemp[investig.HighaTemp$treatment == “hightemperature”, ])) + aes(x=dateTime, y=aTemperature) + geom_point(aes(colour=moats, point=)) + ggtitle(“High Temperature Banding Investigation, High Temperature Treatment”) ylim (10, 20)
hitemp_plot3 ```
# Time series plot
hitemp_plot4 <- ggplot(subset(investig.HighaTemp[investig.HighaTemp$treatment == "allchange", ],
period %in% ("night")),
aes(x=dateTime, y=aTemperature)) +
geom_point(aes(colour=moats, point=)) +
ylim (10, 20)
hitemp_plot1
# Time series plot
hitemp_plot7 <- ggplot(subset(investig.HighaTemp[investig.HighaTemp$treatment == "allchange", ],
period %in% ("day")),
aes(x=dateTime, y=aTemperature)) +
geom_point(aes(colour=moats, point=)) +
ylim (10, 20)
hitemp_plot7
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